Lidar system that generates a point cloud having multiple resolutions

ABSTRACT

An autonomous vehicle having a LIDAR system mounted thereon or incorporated therein is described. The LIDAR system has N channels, with each channel being a light emitter/light detector pair. A computing system identifies M channels that are to be active during a scan of the LIDAR system, wherein M is less than N. The computing system transmits a command signal to the LIDAR system, and the LIDAR system performs a scan with the M channels being active (and N−M channels being inactive). The LIDAR system constructs a point cloud based upon the scan, and the computing system controls the autonomous vehicle based upon the point cloud.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/586,080, filed on Sep. 27, 2019, and entitled “LIDAR SYSTEM THATGENERATES A POINT CLOUD HAVING MULTIPLE RESOLUTIONS”. The entirety ofthis application is incorporated herein by reference.

BACKGROUND

An exemplary LIDAR system mounted on or incorporated in autonomousvehicles may be a spinning LIDAR system that includes an array of lightemitter/light detector pairs arranged vertically along a rotatingspindle of the LIDAR system. Each light emitter/light detector pair isreferred to as a “channel” of the LIDAR systems, and each channelcorresponds to a respective angular range in elevation. For example,each channel may have a range of three degrees, and thus for a LIDARsystem with 16 channels (and assuming no overlap between channels) thevertical field of view of the LIDAR system is 48 degrees in elevation.An angular range in azimuth for each channel depends upon a rotationalvelocity of the spindle and speed of the electronics of the LIDARsystem. In a non-limiting example, the angular range in azimuth for eachmay be five degrees (and thus each light emitter may emit a light pulseat every 5 degrees of rotation of the spindle). This means that each ofthe sixteen light emitters would emit 72 light pulses per rotation,resulting in a point cloud for a single rotation having 1,152 points.

Relatively recently, LIDAR systems with a relatively large number ofchannels have been developed; for example, LIDAR systems with 32channels and 64 channels have been developed, wherein such LIDAR systemsare configured to generate denser point clouds when compared to thosegenerated by LIDAR systems having 16 channels. For instance, withrespect to a LIDAR system having 64 channels, and assuming the sameangular range in azimuth as noted above, such LIDAR system can create apoint cloud for a single rotation having 4,608 points.

Computer-implemented control systems employed in autonomous vehicleshave been developed to process point clouds generated by, for example,LIDAR systems having 16 channels, but have not been developed to processpoint clouds generated by LIDAR systems having 32 channels or 64channels. Updating control systems is a time-consuming task; inaddition, even if such control systems are updated, latency may beintroduced due to the relatively large number of points that suchcontrol systems are to process. Accordingly, when LIDAR systems with therelatively large number of channels are used with autonomous vehicles,conventionally several of the channels are deactivated. Hence, in anexample, with respect to a LIDAR system having 32 channels, every otherone of such channels may be permanently deactivated to effectivelytransform the 32 channel LIDAR system into a conventional 16 channelLIDAR system, such that a control system of an autonomous vehicle canprocess point clouds output by the LIDAR system. This conventionalapproach fails to take advantage of the increased number of channels ofthe LIDAR system.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Described herein are various technologies pertaining to dynamicallyactivating and deactivating channels of a spinning lidar system, whereina channel is a light emitter/light detector pair. With more specificity,described herein is an autonomous vehicle comprising a LIDAR system anda computing system that generates command signals that are configured toactivate and deactivate channels of the LIDAR system. The computingsystem identifies, for a scan of the LIDAR system, a first subset ofchannels of the LIDAR system that are to be active during the scan and asecond subset of channels of the LIDAR system that are to be inactiveduring the scan. Hence, a first portion of a point cloud output by theLIDAR system may have a first resolution and a second portion of thepoint cloud output by the LIDAR system may have a second resolution,wherein the first resolution is greater than the second resolution.Thus, in an example, a first horizontal band in a point cloud thatcorresponds to a region in an environment where objects are more likelyto impact operation of the autonomous vehicle (e.g., a region in thefield of view of the LIDAR system where pedestrians are likely to beobserved) may have the first resolution while a second horizontal bandin the point cloud that corresponds to a periphery of a field of view ofthe LIDAR system may have the second resolution.

With more particularity, the autonomous vehicle has a control systemthat is configured to process point clouds that have a predefined numberof points. When, however, all channels of the LIDAR system are active, apoint cloud generated by the LIDAR system will have a number of pointsthat is greater than the predefined number of points. Rather thanpermanently deactivating some of the channels of the LIDAR system, thecomputing system can, per scan of the LIDAR system, ascertain which ofthe channels are to be active and which of the channels are to beinactive, such that a control system of the autonomous vehicle is ableto take advantage of increased resolution in point clouds due to theincreased number of channels while not requiring the control system tobe redesigned and without introducing latency.

In an exemplary embodiment, the LIDAR system may comprise an array of 32channels that are aligned vertically along a spindle of the LIDARsystem, and the control system of the autonomous vehicle may beconfigured to process point clouds based upon data output by 16 channelsof the LIDAR system. For a scan of the LIDAR system, and based upon, forexample, pitch of the LIDAR system, terrain of the environment (e.g., anupcoming hill), an object identified as being in the environment, etc.,the computing system can identify a range of elevation angles (e.g., −4degrees-10 degrees) with respect to a defined plane and can generate acontrol signal that causes channels that correspond to such range to beactive. Thus, a point cloud generated by the LIDAR system may havehigher resolution with respect to such range of angles when compared toresolution outside of such range of angles. The computing system canupdate the range of angles such that, for example, a roadway beingtraveled upon by the autonomous vehicle is included in the range ofangles on the horizon.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary LIDAR system that is controlled by acomputing system of an autonomous vehicle.

FIG. 2 illustrates an exemplary configuration for data points of a pointcloud.

FIG. 3 illustrates an exemplary configuration for data points of a pointcloud.

FIG. 4 illustrates an exemplary configuration for data points in a pointcloud

FIG. 5 is a schematic that illustrates a LIDAR system of an autonomousvehicle having channels that are selectively controlled by a computingsystem thereof.

FIG. 6 is a functional block diagram of an exemplary autonomous vehicle.

FIG. 7 is a flow diagram illustrating an exemplary methodology formanipulating a resolution within a region of a point cloud having apredefined number of points.

FIG. 8 is a flow diagram illustrating an exemplary methodology foridentifying a channel of a LIDAR system that is to be enabled forgeneration of a point cloud indicative of a surrounding environment.

FIG. 9 illustrates an exemplary computing system.

DETAILED DESCRIPTION

Various technologies pertaining to activating and deactivating channelsof a LIDAR system as an autonomous vehicle travels are now describedwith reference to the drawings, wherein like reference numerals are usedto refer to like elements throughout. In the following description, forpurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of one or more aspects. It maybe evident, however, that such aspect(s) may be practiced without thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form in order to facilitate describing one ormore aspects. Further, it is to be understood that functionality that isdescribed as being carried out by certain system components may beperformed by multiple components. Similarly, for instance, a componentmay be configured to perform functionality that is described as beingcarried out by multiple components.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B.

In addition, the articles “a” and “an” as used in this application andthe appended claims should generally be construed to mean “one or more”unless specified otherwise or clear from the context to be directed to asingular form.

Further, as used herein, the terms “component”, “module”, and “system”are intended to encompass computer-readable data storage that isconfigured with computer-executable instructions that cause certainfunctionality to be performed when executed by a processor. Thecomputer-executable instructions may include a routine, a function, orthe like. It is also to be understood that a component or system may belocalized on a single device or distributed across several devices.

Further, as used herein, the term “exemplary” is intended to meanserving as an illustration or example of something and is not intendedto indicate a preference.

With reference now to FIG. 1 , an environment 100 includes a computingsystem 102 of an autonomous vehicle in communication with a LIDAR system106 that can be mounted on or incorporated in the autonomous vehicle.The LIDAR system 106, in an example, can include a rotating spindle 108that has an array of N channels: N light emitters 110 and correspondingN light detectors (not shown), wherein the LIDAR system 106 is designedsuch that each channel corresponds to an angular range in elevation. Forexample, the LIDAR system 106 may comprise 32 channels, and each channelmay correspond to an angular range of 1.5 degrees in elevation, suchthat the LIDAR system 106 has an angular range of 48 degrees inelevation. The LIDAR system 106 can be a spinning LIDAR system that hasan angular range of 360 degrees in azimuth. While objects of interest ina field of view of the LIDAR system 106 may typically be located, forexample, between −4 degrees and 6 degrees in elevation when theautonomous vehicle is on flat terrain, objects may exist outside of suchrange, and therefore the 32 channels are configured to provide angularcoverage for a larger range of elevation angles.

With more specificity, the computing system 102 may execute a controlsystem 104 that is configured to process point clouds generated by LIDARsystems having M channels, wherein M is less than N. For example, thecomputing system 102 can receive point clouds having 16 horizontal bandsthat correspond to 16 channels, can identify objects in an environmentof the autonomous vehicle based upon the point clouds, and can control amechanical system of the autonomous vehicle (e.g., a propulsion system,a steering system, and/or a braking system) based upon the point clouds.To take advantage of the additional channels (N−M), the computing system102 can include a channel control system 104, wherein the channelcontrol system 104, for each scan (spin) of the LIDAR system 106, isconfigured to identify a set of M channels of the LIDAR system 106 thatare to be active and a set of N−M channels that are to be inactive. Thechannel control system 104 can then cause a command signal to betransmitted to the LIDAR system 106, wherein the LIDAR system 106, uponreceiving the command signal, activates the set of M channels anddeactivates the set of N−M channels identified by the channel controlsystem 104.

The channel control system 104 can identify the set of M channels thatare to be active (and thus the set of N−M channels that are to beinactive) during a scan based upon one or more factors. For instance,the channel control system 104 can identify a set of M channels that areto be active based upon an object being detected in proximity to theautonomous vehicle. With more particularity, the computing system 102can determine that a pedestrian is walking in proximity to theautonomous vehicle and can further determine that the pedestrian isincluded within an angular range in elevation of 6 degrees to 15 degreeswith respect to the LIDAR system 106. Based upon these determinations,the channel control system 104 can identify channels that correspond tosuch angular range, and cause such channels to be active (e.g., suchthat the LIDAR system 106 is “focused” on a horizontal band thatincludes the pedestrian while less “focused” on a horizontal band thatfails to include the pedestrian) during the scan.

In another example, the channel control system 104 can identify a set ofM channels that are to be active based upon a tilt of the LIDAR system106 relative to a horizontal plane. Therefore, when the autonomousvehicle is travelling downhill as the autonomous vehicle enters afreeway (and the tilt relative to the horizontal plane is negative), thechannel control system 104 can identify channels that are to be activesuch that the LIDAR system is “focused” upwards towards the horizon(rather than towards the ground).

In still yet another example, the channel control system 104 canidentify channels that are to be active based upon known terrain inproximity to the autonomous vehicle. For example, a three-dimensionalmap can indicate elevation changes of a roadway upon which theautonomous vehicle is travelling. Based upon such elevation changes, thechannel control system 104 can identify M channels of the N channelLIDAR system 106 that are to be active during the scan (e.g., such thatthe LIDAR system is “focused” with respect to a desired horizontalband).

In a further example, the channel control system 104 can identifychannels that are to be active based upon an output of a perceptionsystem, a direction of travel of the autonomous vehicle, a velocity ofthe autonomous vehicle, an acceleration of the autonomous vehicle, or acombination of the factors noted above. In a specific example, thechannel control system 104 can identify a horizontal band thatcorresponds to emitters 112, where a point cloud generated by the LIDARsystem 106 is to have higher resolution for the horizontal band thananother horizontal band (which corresponds to emitters other than theemitters 112). Thus, rather than every other channel of the LIDAR system106 being active, the channel control system 104 causes the LIDAR system106 to generate a point cloud with different resolutions in differinghorizontal bands. In addition, it can be ascertained that a channel maybe active for one scan of the LIDAR system 106 and then be inactive foran immediately subsequent scan of the LIDAR system 106. Similarly, achannel may be inactive for one scan of the LIDAR system 106 and then beactive for an immediately subsequent scan of the LIDAR system 106.

With reference now to FIGS. 2-4 , point clouds 200, 300, and 400,respectively, represented in two dimensions, are set forth. It can beascertained that the point clouds 200, 300, and 400 include the samenumber of points. However, resolutions in different horizontal bands ofthe point clouds are different. With more specificity, thetwo-dimensional representations of the exemplary point clouds areillustrated as a grid pattern, wherein each vertex of the grid patternrepresents a data point in the point cloud. In particular, eachhorizontal line corresponds to a separate light emitter positionedvertically along the rotating spindle; and each vertical linecorresponds to a degree of rotation of the spindle in the azimuthdirection. A vertex formed via a meeting of a horizontal line withvertical line represents a data point indicative of a light pulseemitted by the LIDAR system.

In FIG. 2 , the point cloud includes a horizontal band 202 that has afirst resolution and horizontal bands 204 and 206 that have a secondresolution that is different from the first resolution. The horizontalband 202 may correspond to a region of interest in the field of view ofthe LIDAR system 106 where higher resolution is desirable when comparedto resolution of regions outside of the horizontal band 202. Forinstance, a higher resolution can be desirable for long rangemeasurements, and the computing system 102 may be able to better detectobjects that are represented in the horizontal band 202 at long range.In an example, the horizontal band 202 may be positioned at amid-section of the point cloud 200 when the autonomous vehicle isnavigating flat terrain and when higher resolution is desirable alongthe horizon.

The horizontal band 202 encompasses an angular range of elevation basedon a first value that defines a bottom of the angular range and a secondvalue that defines a top of the angular range. The bottom of the angularrange corresponds to a bottom of the horizontal band 202 and a top ofthe angular range corresponds to a top of the horizontal band 202. Whenthere are a relatively large number of channels, the channel controlsystem 104 can identify the angular range for the horizontal band andcan further identify a resolution within the horizontal band. Based uponthe angular range and the identified resolution, the channel controlsystem identifies which M of the N channels of the LIDAR system 106 tocause to be active to achieve the desired resolution in the identifiedangular range.

In FIG. 3 , the point cloud 300 includes two horizontal bands 302-304,which comprise a first horizontal band 302 that has a first (high)resolution and a second horizontal band 304 that has a second (low)resolution. The first horizontal band 302 is positioned at an upperportion of the point cloud 300 and the second horizontal band 304 ispositioned at a lower portion of the point cloud 300. For instance, ifthe autonomous vehicle were to navigate an uphill grade, the firsthorizontal band 302 may correspond to an upward trajectory of the road(e.g., toward the top of the hill) such that a higher resolution ismaintained along the horizon of the road.

The computing system 102, as indicated previously, can determine a tiltof the LIDAR system 106 relative to a horizontal plane and can determineangular ranges (horizontal bands) where different resolutions aredesired based upon the tilt, and can subsequently identify which M ofthe N channels of the LIDAR system 106 to cause to be active for a scanand which N−M channels of the autonomous vehicle to cause to be inactivefor the scan. The pitch of the LIDAR system may be computed forindividual scans of the environment or an angular range of the pluralityof dense regions 302-304 may be extended based on a predetermined numberthat accounts for a pitch of the LIDAR system in general.

In FIG. 4 , the point cloud 400 similarly includes two horizontal bands402-404, which comprise a first horizontal band 402 that has a first(high) resolution and a second horizontal band 404 that has a second(low) resolution. The first horizontal band 402 is positioned at a lowerportion of the point cloud 400 and the second horizontal band 404 ispositioned at an upper portion of the point cloud 400. For instance, ifthe autonomous vehicle were to navigate a downhill grade, the firsthorizontal band 402 may correspond to a downward trajectory of the road(e.g., toward the base of the hill) such that a higher resolution ismaintained along the horizon of the road. As noted, the computing system102 can determine a tilt of the LIDAR system 106 relative to ahorizontal plane to determine angular ranges (horizontal bands) wheredifferent resolutions are desired based upon the tilt. In particular,the computing system 102 can identify which M of the N channels shouldbe active for a scan of the LIDAR system 106 and which N−M channelsshould be inactive for the scan of the LIDAR system 106.

It should be appreciated from the foregoing that manipulating aresolution of various bands of a point cloud, as represented by theexemplary point clouds 200, 300, and 400, includes advantages beyondnavigating uphill and downhill grades. For instance, navigating opensspaces at high speeds can require the first (high) resolution band to belocated at a different position in the point cloud than when navigatingtight spaces at low speeds (e.g., inside a parking garage).Additionally, a point cloud generated using only M of the N channelsreduces processing demands on the computing system 102 when compared toa computing system that is required to process all N channel.

With reference now to FIG. 5 , an exemplary environment 500 isillustrated that includes an autonomous vehicle 502 having the LIDARsystem 106 affixed thereto. The autonomous vehicle 502 includescomponentry depicted in call-out 508. Hence, the autonomous vehicle 502comprises the LIDAR system 106 which emits light pulses into thesurrounding environment 500, a mechanical system 504 (e.g., a vehiclepropulsion system, a steering system, a braking system, etc.), and thecomputing system 102 that is configured to identify M channels fromamongst the N channels of the LIDAR system 106 to activate for each scanof the LIDAR system 106. The computing system 102 is in communicationwith the LIDAR system 106 and the mechanical system 108 and comprisesthe channel control system 104, as described above.

The computing system 102 can identify a pedestrian 506 in a field ofview of the LIDAR system 106 based upon signals output by sensor systemof the autonomous vehicle 502, including the LIDAR system 106. Thecomputing system 102 can compute an angular range in elevation for theLIDAR system 106, wherein such range encompasses the pedestrian 506 inthe field of view of the LIDAR system 106. The computing system 102 canthen identify channels of the LIDAR system 106 that correspond to suchangular range, and can further (optionally) determine a desiredresolution of the point cloud for such angular range. The computingsystem 102 can then transmit a command signal to the LIDAR system 106that indicates which of the N channels are to be active and which of theN channels are to be inactive during the scan, and the LIDAR system 106can perform the scan based upon the command signal.

In addition, in embodiments, the autonomous vehicle 502 can include acamera sensor system that generates an image signal that includes thepedestrian 506. The image signal can be provided to the computing system102, wherein the region can be identified in the field of view based onfeatures captured in the image signal.

With reference now to FIG. 6 , a functional block diagram of theautonomous vehicle 502 is illustrated. The autonomous vehicle 502operates based on sensor signals output by LIDAR systems (106, 601) aswell as by other sensor systems 602-604. Accordingly, the autonomousvehicle 502 includes a plurality of sensor systems, for example, a firstsensor system 602 through a Pth sensor system 604, which can include afirst LIDAR system 106 through an Pth LIDAR system 601.

The sensor systems 602-604 can be of different types and are arrangedabout the autonomous vehicle 502. For example, the first sensor system602 may be a camera sensor system and the Pth sensor system 604 may be aradar sensor system. Other exemplary sensor systems include globalpositioning system (GPS) sensor systems, inertial measurement unit (IMU)sensor systems, infrared sensor systems, sonar sensor systems, and thelike. Furthermore, some or all of the of sensor systems 602-604 may bearticulating sensors that can be oriented or rotated such that a fieldof view of the articulating sensors is directed towards different areassurrounding the autonomous vehicle 502.

The autonomous vehicle 502 further includes several mechanical systemsthat can be used to effectuate appropriate motion of the autonomousvehicle 502. For instance, the mechanical systems can include but arenot limited to a vehicle propulsion system 606, a braking system 608,and a steering system 610. The vehicle propulsion system 606 may includean electric motor, an internal combustion engine, or both. The brakingsystem 608 can include an engine brake, actuators, and/or any othersuitable componentry that is configured to assist in decelerating theautonomous vehicle 502. The steering system 610 includes suitablecomponentry that is configured to control the direction of movement ofthe autonomous vehicle 502 during propulsion.

The autonomous vehicle 502 additionally comprises the computing system102, which is in communication with the sensor systems 602-604 (e.g.,the LIDAR systems (106, 601)) and the mechanical systems 606-610. Thecomputing system 102 comprises a data store 620 having geolocation data622 stored therein, a processor 612, and memory 614 that includesinstructions that are executed by the processor 612. In an example, theprocessor 612 can be or include a graphics processing unit (GPU), aplurality of GPUs, a central processing unit (CPU), a plurality of CPUs,an application-specific integrated circuit (ASIC), a microcontroller, aprogrammable logic controller (PLC), a field programmable gate array(FPGA), or the like.

Memory 614 includes the channel control system 104, wherein the channelcontrol system 104 further comprises a region of interest module 616 anda channel identifier module 618. The region of interest module 616 isexecuted by the processor 612 to identify one or more regions ofinterest in a field of view of the LIDAR systems (106, 601), whereresolution of a point cloud generated by a LIDAR system is to be higherin the region of interest than resolution of the point cloud outside theregion of interest. The region of interest may be identified based onthe geolocation data 622, from an image signal provided by a camerasensor system, based upon data output by another sensor system, or thelike. For instance, the geolocation data 622 may correspond to map dataindicative of features/objects located in the field of view of the LIDARsystems (106, 601).

The channel identifier module 618 is executed by the processor 612 toidentify channels of the LIDAR systems (106, 601) that emit light pulseswhich correspond to locations inside the region of interest. Uponidentification of the corresponding channels, the channel control system104 can control a number of the corresponding channels to emit lightpulses based on a desired resolution of the point cloud inside theregion of interest. In an example, the computing system 102 may beconfigured to process point clouds generated based upon output of 16channels of a LIDAR system; however, the LIDAR system 106 may comprise32 channels, 8 of which correspond to the region of interest identifiedby the region of interest module 616. In this example, the channelidentifier module 618 can identify the 8 channels that correspond to theregion of interest, such that the control system 104 may control theLIDAR system 106 to cause the 8 channels to be active (as well as 8other channels that may be evenly spaced from one another outside theregion of interest) to output a point cloud that is generated based onoutput of 16 channels of the LIDAR system 106.

FIGS. 7-8 illustrate exemplary methodologies relating to generating apoint cloud having horizontal bands with different resolutions therein.While the methodologies are shown and described as being a series ofacts that are performed in a sequence, it is to be understood andappreciated that the methodologies are not limited by the order of thesequence. For example, some acts can occur in a different order thanwhat is described herein. In addition, an act can occur concurrentlywith another act. Further, in some instances, not all acts may berequired to implement a methodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

Referring now to FIG. 7 , an exemplary methodology 700 for controlling aLIDAR system is illustrated. The methodology 700 can be performed by thecomputing system 102 of the autonomous vehicle 502. The methodology 700starts at 702, and at 704 for a scan of a LIDAR system, a region isidentified in a field of view of the LIDAR system, wherein resolution ina first horizontal band of a point cloud to be generated by the LIDARsystem is greater than resolution in a second horizontal band of thepoint cloud, wherein the first horizontal band corresponds to theregion. At 706, a first subset of channels of the LIDAR system that areto be active are identified based upon the identified region, and asecond subset of the channels of the LIDAR system that are to beinactive are identified based upon the identified region. Hence, in apoint cloud generated by the LIDAR system with respect to the scan, thepoint cloud will include a first horizontal band that has a firstresolution and a second horizontal band that has a second resolution,wherein the first horizontal band encompasses the region, and furtherwherein the first resolution is higher than the second resolution. At708, a command signal is transmitted to the LIDAR system that causes theLIDAR system to perform the scan with the first subset of channels beingactive and the second subset of channels being inactive. The methodology700 returns to 704 for a subsequent scan.

Referring now to FIG. 8 , an exemplary methodology 800 for performing aLIDAR scan is illustrated, wherein the methodology 800 is performed bythe LIDAR system 106. The methodology 800 starts at 802, and at 804 theLIDAR system receives a command signal from a computing system. Thecommand signal includes an indication, for each channel of the LIDARsystem, as to whether the channel is to be active or inactive for thescan. At 806, the LIDAR system performs a scan of its environment basedupon the command signal, wherein a first subset of channels of the LIDARsystem are active and a second subset of channels of the LIDAR systemare inactive. At 808, the LIDAR system generates a point cloud basedupon the scan, wherein the point cloud includes a first horizontal bandthat has a first resolution and a second horizontal band that has asecond resolution, wherein the first resolution is different from thesecond resolution. The methodology 800 returns to 804 with respect to asubsequent scan.

Referring now to FIG. 9 , a high-level illustration of an exemplarycomputing device 900 that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. For instance, thecomputing device 900 may be or include the computing system 102. Thecomputing device 900 includes at least one processor 902 that executesinstructions that are stored in a memory 904. The instructions may be,for instance, instructions for implementing functionality described asbeing carried out by one or more components discussed above orinstructions for implementing one or more of the methods describedabove. The processor 902 may access the memory 904 by way of a systembus 906. In addition to storing executable instructions, the memory 904may also store information related to geospatial position data, LIDARchannel identifiers, and the like.

The computing device 900 additionally includes a data store 908 that isaccessible by the processor 902 by way of the system bus 906. The datastore 908 may include executable instructions, geolocation data, pointclouds, and the like. The computing device 900 also includes an inputinterface 910 that allows external devices to communicate with thecomputing device 900. For instance, the input interface 910 may be usedto receive instructions from an external computer device, from a user,etc. The computing device 900 also includes an output interface 912 thatinterfaces the computing device 900 with one or more external devices.For example, the computing device 900 may transmit control signals tothe LIDAR systems (106, 601) and other sensor systems 602-604, thevehicle propulsion system 606, the braking system 608, and/or thesteering system 610 by way of the output interface 912.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 900 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 900.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and Blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionally described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the details description or the claims,such term is intended to be inclusive in a manner similar to the term“comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A LIDAR system comprising: N light emitters; andN light detectors that respectively correspond to the N light emitterssuch that the LIDAR system comprises N channels, wherein for a scan ofthe LIDAR system the LIDAR system is configured to: deactivate Mchannels of the LIDAR system such that N−M channels of the LIDAR systemare active for the scan, wherein N is greater than M; and generate apoint cloud based upon the N−M channels being active and the M channelsbeing inactive, wherein the point cloud is indicative of location ofobjects relative to the LIDAR system, and further wherein the pointcloud includes points that have differing resolutions.
 2. The LIDARsystem of claim 1, wherein the LIDAR system is further configured totransmit the point cloud to a computing system of an autonomous vehicle(AV), wherein the computing system of the AV causes the AV to perform adriving maneuver based upon the point cloud.
 3. The LIDAR system ofclaim 1, wherein N is 32 and M is
 16. 4. The LIDAR system of claim 1,wherein the point cloud comprises a first horizontal band and a secondhorizontal band that is non-overlapping with the first horizontal band,wherein the first horizontal band has a first resolution and the secondhorizontal band has a second resolution that is different from the firstresolution.
 5. The LIDAR system of claim 1, wherein at least two of theN−M channels that are active are vertically adjacent to one another on aspindle of the LIDAR system.
 6. The LIDAR system of claim 1, wherein theLIDAR system deactivates the M channels based upon a signal receivedfrom a computing system that is in communication with the LIDAR system.7. The LIDAR system of claim 1, where M is equal to N/2.
 8. The LIDARsystem of claim 1, wherein the N channels are aligned vertically along aspindle of the LIDAR system.
 9. The LIDAR system of claim 1, wherein theLIDAR system is further configured to: for a second scan of the LIDARsystem that is subsequent the scan, activating a channel in the Nchannels that was deactivated during the scan.
 10. The LIDAR system ofclaim 1, wherein the LIDAR system is further configured to: for a secondscan of the LIDAR system that is subsequent the scan, deactivating achannel in the N channels that was activated during the scan.
 11. TheLIDAR system of claim 1 being incorporated into an autonomous vehicle.12. A method performed by a LIDAR system having N channels, the methodcomprising: performing a first scan of an environment of the LIDARsystem, wherein a first point cloud is generated for the first scanbased upon output of a first light detector in a first channel of theLIDAR system; immediately subsequent to performing the first scan of theenvironment, deactivating the first channel of the LIDAR system andactivating a second channel of the LIDAR system that was deactivatedduring the first scan; and performing a second scan of the environmentof the LIDAR system, wherein a second cloud is generated for the secondscan based upon output of a second light detector in the second channelof the LIDAR system.
 13. The method of claim 12, wherein the firstchannel of the LIDAR system and the second channel of the LIDAR systemare adjacent to one another along a spindle of the LIDAR system.
 14. Themethod of claim 12, wherein the LIDAR system includes N channels, andfurther wherein during each of the first scan and the second scan M ofthe N channels are deactivated, where M is greater than 0 and less thanN.
 15. The method of claim 14, where N is 32 and M is
 16. 16. The methodof claim 14, where N is 64 and M is
 32. 17. The method of claim 12,wherein the LIDAR system is a spinning LIDAR system.
 18. The method ofclaim 12, further comprising: transmitting the first point cloud and thesecond point cloud to a computing system of an autonomous vehicle (AV),wherein the AV performs a driving maneuver based upon the first pointcloud and the second point cloud.
 19. A spinning LIDAR systemcomprising: N channels, wherein each channel is a light emitter/lightdetector pair, wherein the spinning LIDAR system is configured toperform acts comprising: activating first channels from amongst the Nchannels for a first scan of an environment of the LIDAR system;performing the first scan of the environment of the LIDAR system whenthe first channels are activated, wherein the LIDAR system, for thefirst scan, generates a first point cloud based upon outputs of thefirst channels; immediately subsequent to performing the first scan ofthe environment of the LIDAR system, activating second channels fromamongst the N channels for a second scan of the environment of the LIDARsystem, wherein the second channels include a channel not included inthe first channels; and performing the second scan of the environment ofthe LIDAR system when the second channels are activated, wherein theLIDAR system, for the second scan, generates a second point cloud basedupon outputs of the second channels.
 20. The spinning LIDAR system ofclaim 19, wherein a second channel is included in both the firstchannels and the second channels.